Noisy OR CPDΒΆ
Initializes the NoisyORCPD class.
The NoisyOR CPD is a special case of TabularCPD for binary variables where a given variable can be activated by each of the parent variables with a specified probability. This activation probability is defined in the prob_values argument.
- param variable:
The variable for which the CPD is to be defined.
- type variable:
str
- param prob_values:
A list of probabilities values for each evidence variable to activate variable.
- type prob_values:
iterable
- param evidence:
List of evidence variables, i.e., conditional variables.
- type evidence:
list
Examples
>>> from pgmpy.factors.discrete import NoisyORCPD
>>> cpd = NoisyORCPD(variable="Y", prob_values=[0.3, 0.5], evidence=["X1", "X2"])
>>> from pgmpy.models import DiscreteBayesianNetwork
>>> model = DiscreteBayesianNetwork(
... [("A", "B")]
... ) # With nodes with no parents, we can not define a NoisyORCPD.
>>> cpd_a = TabularCPD(
... variable="A",
... variable_card=2,
... values=[[0.2], [0.8]],
... state_names={"A": ["True", "False"]},
... )
>>> cpd_b = NoisyORCPD(variable="B", prob_values=[0.8], evidence=["A"])
>>> model.add_cpds(cpd_a, cpd_b)